{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:21:37Z","timestamp":1777126897737,"version":"3.51.4"},"reference-count":153,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Path planning is a complex task in robotics, requiring an efficient and adaptive algorithm to find the shortest path in a dynamic environment. The traditional path planning methods, such as graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms, have limitations in computational efficiency, real-time adaptability, and obstacle avoidance. To address these challenges, hybrid path planning algorithms combine the strengths of multiple techniques to enhance performance. This paper includes a comprehensive review of hybrid approaches based on graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms. Also, this article discusses the advantages and limitations, supported by a comparative evaluation of computational complexity, path optimization, and finding the shortest path in a dynamic environment. Finally, we propose an AI-driven adaptive path planning approach to solve the difficulties.<\/jats:p>","DOI":"10.3390\/jsan14050087","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T09:08:52Z","timestamp":1756458532000},"page":"87","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hybrid Path Planning Algorithm for Autonomous Mobile Robots: A Comprehensive Review"],"prefix":"10.3390","volume":"14","author":[{"given":"Mithun","family":"Shanmugaraja","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4866-1428","authenticated-orcid":false,"given":"Mohanraj","family":"Thangamuthu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9255-9631","authenticated-orcid":false,"given":"Sivasankar","family":"Ganesan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, S., Tang, W., Li, P., and Zha, F. (2024). Mapless Path Planning for Mobile Robot Based on Improved Deep Deterministic Policy Gradient Algorithm. Sensors, 24.","DOI":"10.3390\/s24175667"},{"key":"ref_2","unstructured":"Sreerag, S., and Lekshmi, R.R. (2024, January 14\u201316). Allocation of Medicine Dispensing Mobile Robot Considering Path Plan and Battery SoC. Proceedings of the 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106076","DOI":"10.1016\/j.asoc.2020.106076","article-title":"Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm","volume":"89","author":"Ajeil","year":"2020","journal-title":"Appl. Soft Comput. J."},{"key":"ref_4","first-page":"4481","article-title":"Mobile robot path planning and obstacle avoidance using hybrid algorithm","volume":"15","author":"Mohanraj","year":"2023","journal-title":"Int. J. Inf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pudugosula, H., and Kochuvila, S. (2024, January 24\u201325). Path Planning of Robots Using Classical Reinforcement Learning Approach. Proceedings of the 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC), Davangere, India.","DOI":"10.1109\/ICICEC62498.2024.10808858"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"154","DOI":"10.54097\/hset.v46i.7697","article-title":"Research on the A Star Algorithm for Finding Shortest Path","volume":"46","author":"Yan","year":"2023","journal-title":"Highlights Sci. Eng. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.proeng.2014.12.098","article-title":"Path Planning with Modified a Star Algorithm for a Mobile Robot","volume":"96","author":"Babinec","year":"2014","journal-title":"Procedia Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, L., Qin, J., Wu, Z., Duan, L., Li, Z., Cao, M., Ou, X., Su, X., and Li, W. (2015, January 8\u201310). Path planning of automated guided vehicles based on improved A-Star algorithm. Proceedings of the 2015 IEEE International Conference on Information and Automation, Lijiang, China.","DOI":"10.1109\/ICInfA.2015.7279630"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, D., Liu, Q., Yang, J., and Huang, D. (2024). Research on path planning for intelligent mobile robots based on improved A* algorithm. Symmetry, 16.","DOI":"10.3390\/sym16101311"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"122922","DOI":"10.1016\/j.eswa.2023.122922","article-title":"Research on global path planning algorithm for mobile robots based on improved A","volume":"243","author":"Xu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, C., Wu, H., and Wei, Y. (2023). Mobile robot path planning based on kinematically constrained A-star algorithm and DWA fusion algorithm. Mathematics, 11.","DOI":"10.3390\/math11214552"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"57736","DOI":"10.1109\/ACCESS.2022.3179397","article-title":"A mobile robot path planning algorithm based on improved A* algorithm and dynamic window approach","volume":"10","author":"Li","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/j.proeng.2012.01.110","article-title":"The improved dijkstra\u2019s shortest path algorithm and its application","volume":"29","year":"2012","journal-title":"Procedia Engineering"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Verma, D., Messon, D., Rastogi, M., and Singh, A. (2021, January 19\u201320). Comparative Study of Various Approaches of Dijkstra Algorithm. Proceedings of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS51004.2021.9397200"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"413","DOI":"10.18196\/jrc.v4i3.18489","article-title":"Application of Odometry and Dijkstra algorithm as navigation and shortest path determination system of warehouse mobile robot","volume":"4","author":"Ubaidillah","year":"2023","journal-title":"J. Robot. Control (JRC)"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/s41598-021-04506-y","article-title":"Path planning and smoothing of mobile robot based on improved artificial fish swarm algorithm","volume":"12","author":"Li","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_17","first-page":"24","article-title":"Towards effective strategies for mobile robot using reinforcement learning and graph algorithms","volume":"15","author":"Shaposhnikova","year":"2023","journal-title":"Autom. Technol. Bus. Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Beamer, S., Asanovic, K., and Patterson, D. (2012, January 10\u201316). Direction-optimizing Breadth-First Search. Proceedings of the SC \u201812: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, Salt Lake City, UT, USA.","DOI":"10.1109\/SC.2012.50"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/TRO.2023.3339989","article-title":"A novel graph-based motion planner of multi-mobile robot systems with formation and obstacle constraints","volume":"40","author":"Liu","year":"2023","journal-title":"IEEE Trans. Robot."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Biju, A., Gayathri, M.P., Jayapandian, N., Chris, A., and Thaleeparambil, N.R. (2025, January 7\u20139). Efficient Pathfinding in a Maze to overcome Challenges in Robotics and AI Using Breadth-First Search. Proceedings of the 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India.","DOI":"10.1109\/CSNT64827.2025.10968383"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, Q., Xie, F., Zhao, J., Xu, B., Yang, J., Liu, X., and Suo, H. (2022). FPS: Fast path planner algorithm based on sparse visibility graph and bidirectional breadth-first search. Remote Sens., 14.","DOI":"10.3390\/rs14153720"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17298806221116483","DOI":"10.1177\/17298806221116483","article-title":"A coverage path planning approach for autonomous radiation mapping with a mobile robot","volume":"19","author":"Sahari","year":"2022","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Deng, X., Jiao, T., Qin, X., Wang, Y., Zheng, Q., Hou, Z., Zhen, Y., and Li, W. (2024, January 19\u201324). Radiation Mapping based on DFS and Gaussian Process Regression. Proceedings of the 2024 4th URSI Atlantic Radio Science Meeting (AT-RASC), Meloneras, Spain.","DOI":"10.46620\/URSIATRASC24\/XPFL6004"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Han, Z., Sun, H., Huang, J., Xu, J., Tang, Y., and Liu, X. (2024). Path planning algorithms for smart parking: Review and prospects. World Electr. Veh. J., 15.","DOI":"10.3390\/wevj15070322"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.dt.2023.04.012","article-title":"Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm","volume":"32","author":"Zhou","year":"2024","journal-title":"Def. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104630","DOI":"10.1016\/j.robot.2024.104630","article-title":"Path planning algorithms in the autonomous driving system: A comprehensive review","volume":"174","author":"Reda","year":"2024","journal-title":"Robot. Auton. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Champagne Gareau, J., Beaudry, \u00c9., and Makarenkov, V. (2023). Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function. Front. Robot. AI, 9.","DOI":"10.3389\/frobt.2022.1076897"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nordin, N.A., Shariff, S.S., Supadi, S.S., and Masudin, I. (2024). Modelling the Shortest Path for Inner Warehouse Travelling Using the Floyd\u2013Warshall Algorithm. Mathematics, 12.","DOI":"10.3390\/math12172698"},{"key":"ref_29","first-page":"2873","article-title":"Floyd-Warshall Algorithm Based on Picture Fuzzy Information","volume":"136","author":"Habib","year":"2023","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"77","DOI":"10.4114\/intartif.vol25iss70pp77-94","article-title":"Autonomous UAV object Avoidance with Floyd-warshall differential evolution approach","volume":"25","author":"NageswaraGuptha","year":"2022","journal-title":"Intel. Artif."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"119993","DOI":"10.1109\/ACCESS.2023.3326756","article-title":"Path planning of inspection robot based on improved intelligent water drop algorithm","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, H., Yang, X., Gao, Y., Cui, X., and Wang, B. (2022). Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm. Front. Neurorobotics, 16.","DOI":"10.3389\/fnbot.2022.955179"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Katona, K., Neamah, H.A., and Korondi, P. (2024). Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot. Sensors, 24.","DOI":"10.3390\/s24113573"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"60253","DOI":"10.1109\/ACCESS.2022.3181131","article-title":"Field Evaluation of Path-Planning Algorithms for Autonomous Mobile Robot in Smart Farms","volume":"10","author":"Pak","year":"2022","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2319394","DOI":"10.1080\/23311916.2024.2319394","article-title":"Comparative analysis of Bellman-Ford and Dijkstra\u2019s algorithms for optimal evacuation route planning in multi-floor buildings","volume":"11","author":"Bhat","year":"2024","journal-title":"Cogent Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Alamoudi, O., and Al-Hashimi, M. (2024). On the Energy Behaviors of the Bellman\u2013Ford and Dijkstra Algorithms: A Detailed Empirical Study. J. Sens. Actuator Netw., 13.","DOI":"10.3390\/jsan13050067"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, X., and Tong, Y. (2023). Path planning of a mobile robot based on the improved RRT algorithm. Appl. Sci., 14.","DOI":"10.3390\/app14010025"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hao, K., Yang, Y., Li, Z., Liu, Y., and Zhao, X. (2023). CERRT: A mobile robot path planning algorithm based on RRT in complex environments. Appl. Sci., 13.","DOI":"10.3390\/app13179666"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Varricchio, V., Chaudhari, P., and Frazzoli, E. (June, January 31). Sampling-based algorithms for optimal motion planning using process algebra specifications. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907642"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, J., and Zheng, E. (2024). Path planning of a mobile robot based on the improved rapidly exploring random trees star algorithm. Electronics, 13.","DOI":"10.3390\/electronics13122340"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"035212","DOI":"10.1088\/2631-8695\/ad61bd","article-title":"Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL) for mobile robot","volume":"6","author":"Ayalew","year":"2024","journal-title":"Eng. Res. Express"},{"key":"ref_42","first-page":"190","article-title":"Memorized rapidly exploring random tree optimization (mrrto): An enhanced algorithm for robot path planning","volume":"24","author":"Muhsen","year":"2024","journal-title":"Cybern. Inf. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12011","DOI":"10.3934\/math.2024587","article-title":"GAO-RRT*: A path planning algorithm for mobile robot with low path cost and fast convergence","volume":"9","author":"Zhu","year":"2024","journal-title":"AIMS Math."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Qiao, L., Luo, X., and Luo, Q. (2022). An Optimized Probabilistic Roadmap Algorithm for Path Planning of Mobile Robots in Complex Environments with Narrow Channels. Sensors, 22.","DOI":"10.3390\/s22228983"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Alarabi, S., Luo, C., and Santora, M. (2022, January 18\u201320). A PRM Approach to Path Planning with Obstacle Avoidance of an Autonomous Robot. Proceedings of the 2022 8th International Conference on Automation, Robotics and Applications (ICARA), Prague, Czech Republic.","DOI":"10.1109\/ICARA55094.2022.9738559"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"243","DOI":"10.37917\/ijeee.20.2.21","article-title":"Efficient Path Planning in Medical Environments: Integrating Genetic Algorithm and Probabilistic Roadmap (GA-PRM) for Autonomous Robotics","volume":"20","author":"Sabeeh","year":"2024","journal-title":"Iraqi J. Electr. Electron. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"081002","DOI":"10.1115\/1.4068407","article-title":"Risk-Bounded and Probabilistic Roadmap-Based Motion Planner for Arbitrarily Shaped Robots With Uncertainty","volume":"25","author":"Stone","year":"2025","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102043","DOI":"10.1016\/j.rineng.2024.102043","article-title":"Optimizing path planning in mobile robot systems using motion capture technology","volume":"22","author":"Szabolcsi","year":"2024","journal-title":"Results Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.cogr.2024.06.001","article-title":"POMDP-based probabilistic decision making for path planning in wheeled mobile robot","volume":"4","author":"Deshpande","year":"2024","journal-title":"Cogn. Robot."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1017\/S0263574725000281","article-title":"Comparative analysis of popular mobile robot roadmap path-planning methods","volume":"43","author":"Ayawli","year":"2025","journal-title":"Robotica"},{"key":"ref_51","unstructured":"Swedeen, J., and Droge, G. (2023). Batch Informed Trees (BIT*). arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1177\/0278364912444543","article-title":"Cross-entropy motion planning","volume":"31","author":"Kobilarov","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"6912","DOI":"10.1109\/LRA.2025.3572809","article-title":"A Kinematic Constrained Batch Informed Trees algorithm with varied density sampling for mobile robot path planning","volume":"10","author":"Wang","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1177\/0278364919890396","article-title":"Batch informed trees (bit*): Informed asymptotically optimal anytime search","volume":"39","author":"Gammell","year":"2020","journal-title":"Int. J. Robot. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, L., Bing, Z., Chen, K., Chen, L., Cai, K., Zhang, Y., Wu, F., Krumbholz, P., Yuan, Z., and Haddadin, S. (2024, January 14\u201318). Flexible informed trees (FIT*): Adaptive batch-size approach in informed sampling-based path planning. Proceedings of the 2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/IROS58592.2024.10802466"},{"key":"ref_56","unstructured":"Gammell, J.D., Srinivasa, S.S., and Barfoot, T.D. (2014, January 12\u201313). BIT*: Sampling-based optimal planning via batch informed trees. Proceedings of the The Information-Based Grasp and Manipulation Planning Workshop, Robotics: Science and Systems (RSS), Berkeley, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1177\/0278364906067174","article-title":"On the Probabilistic Foundations of Probabilistic Roadmap Planning","volume":"25","author":"Hsu","year":"2006","journal-title":"Int. J. Robot. Res."},{"key":"ref_58","unstructured":"Bruce, J., and Veloso, M. (October, January 30). Real-time randomized path planning for robot navigation. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wu, D., Wei, L., Wang, G., Tian, L., and Dai, G. (2022). APF-IRRT*: An Improved Informed Rapidly-Exploring Random Trees-Star Algorithm by Introducing Artificial Potential Field Method for Mobile Robot Path Planning. Appl. Sci., 12.","DOI":"10.3390\/app122110905"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.32604\/iasc.2023.028126","article-title":"Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction","volume":"35","author":"Sivaranjani","year":"2023","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Siming, W., Tiantian, Z., and Weijie, L. (2018, January 24\u201327). Mobile Robot Path Planning Based on Improved Artificial Potential Field Method. Proceedings of the 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), Lanzhou, China.","DOI":"10.1109\/IRCE.2018.8492951"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.isatra.2023.02.018","article-title":"Robot path planning based on artificial potential field with deterministic annealing","volume":"138","author":"Wu","year":"2023","journal-title":"ISA Trans."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1109\/LRA.2023.3290819","article-title":"Safe artificial potential field-novel local path planning algorithm maintaining safe distance from obstacles","volume":"8","author":"Szczepanski","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Yang, C., Pan, J., Wei, K., Lu, M., and Jia, S. (2024). A novel unmanned surface vehicle path-planning algorithm based on A* and artificial potential field in ocean currents. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12020285"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"24708","DOI":"10.1109\/JSEN.2024.3410271","article-title":"Research and validation of self-driving path planning algorithm based on optimized A*-artificial potential field method","volume":"24","author":"Shan","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/100.580977","article-title":"The Dynamic Window Approach to Collision Avoidance","volume":"4","author":"Fox","year":"1997","journal-title":"Robot. Autom. Mag. IEEE"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kuderer, M., Gulati, S., and Burgard, W. (2015, January 26\u201330). Learning driving styles for autonomous vehicles from demonstration. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, DC, USA.","DOI":"10.1109\/ICRA.2015.7139555"},{"key":"ref_68","unstructured":"Khatib, O. (1985, January 25\u201328). Real-time obstacle avoidance for manipulators and mobile robots. Proceedings of the 1985 IEEE International Conference on Robotics and Automation, St. Louis, MO, USA."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Lin, Z., and Taguchi, R. (2023). Faster implementation of the dynamic window approach based on non-discrete path representation. Mathematics, 11.","DOI":"10.3390\/math11214424"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/LRA.2023.3257681","article-title":"Safe and efficient dynamic window approach for differential mobile robots with stochastic dynamics using deterministic sampling","volume":"8","author":"Yasuda","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3068","DOI":"10.1109\/TRO.2024.3400932","article-title":"Dynamic adaptive dynamic window approach","volume":"40","author":"Dobrevski","year":"2024","journal-title":"IEEE Trans. Robot."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Borroni, C.G., Cazzaro, M., and Chiodini, P.M. (2025). The Dynamic Window Approach as a Tool to Improve Performance of Nonparametric Self-Starting Control Charts. Mathematics, 13.","DOI":"10.3390\/math13060938"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1109\/TCST.2017.2705059","article-title":"A Guiding Vector-Field Algorithm for Path-Following Control of Nonholonomic Mobile Robots","volume":"26","author":"Kapitanyuk","year":"2018","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Boroujeni, Z., Mohammadi, M., Neumann, D., Goehring, D., and Rojas, R. (2018, January 26\u201330). Autonomous Car Navigation Using Vector Fields. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Suzhou, China.","DOI":"10.1109\/IVS.2018.8500446"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ifacol.2022.04.016","article-title":"Algorithms for Path Planning on Mobile Robots","volume":"55","author":"Jogeshwar","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"51","DOI":"10.30564\/jcsr.v6i4.7197","article-title":"Optimization of Mobile Robot Delivery System Based on Deep Learning","volume":"6","author":"Chen","year":"2024","journal-title":"J. Comput. Sci. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/S0303-2647(97)01708-5","article-title":"Ant colonies for the travelling salesman problem","volume":"43","author":"Dorigo","year":"1997","journal-title":"Biosystems"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/TASE.2022.3212428","article-title":"Research on Terminal Distance Index-Based Multi-Step Ant Colony Optimization for Mobile Robot Path Planning","volume":"20","author":"Li","year":"2023","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1007\/s12065-023-00821-7","article-title":"Improved ant colony algorithm in path planning of a single robot and multi-robots with multi-objective","volume":"17","author":"Pu","year":"2024","journal-title":"Evol. Intell."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Li, P., Wei, L., and Wu, D. (2025). An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications. Sensors, 25.","DOI":"10.3390\/s25051326"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"101974","DOI":"10.1016\/j.jksuci.2024.101974","article-title":"Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy","volume":"36","author":"Tao","year":"2024","journal-title":"J. King Saud. Univ.-Comput. Inf. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neucom.2021.12.016","article-title":"A new approach to smooth path planning of mobile robot based on quartic Bezier transition curve and improved PSO algorithm","volume":"473","author":"Xu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1040461","DOI":"10.1155\/2023\/1040461","article-title":"Trajectory Planning and Collision Control of a Mobile Robot: A Penalty-Based PSO Approach","volume":"2023","author":"Pandey","year":"2023","journal-title":"Math. Probl. Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.procs.2018.01.113","article-title":"Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning","volume":"127","author":"Lamini","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhang, T., Pan, X., and Hu, M. (2019, January 27\u201330). Multi-objective mobile robot path planning algorithm based on adaptive genetic algorithm. Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China.","DOI":"10.23919\/ChiCC.2019.8865455"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Yang, J., Kang, M., Lee, S., and Kim, S. (2025). Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains. Mathematics, 13.","DOI":"10.3390\/math13050810"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhao, Z., Yu, L., Zhang, J., Lian, J., and Wu, F. (2024, January 28\u201331). A Hybrid Multi-objective Heuristic Algorithm for Automated Guided Vehicle Path Planning. Proceedings of the 2024 43rd Chinese Control Conference (CCC), Kunming, China.","DOI":"10.23919\/CCC63176.2024.10661500"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Dai, X., Liu, C., Lai, Q., Huang, X., Zeng, Q., and Liu, M. (2025). The Local Path Planning Algorithm for Amphibious Robots Based on an Improved Dynamic Window Approach. J. Mar. Sci. Eng., 13.","DOI":"10.3390\/jmse13030399"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Baek, Y., and Park, J.K. (2025). Fast Path Generation Algorithm for Mobile Robot Navigation Using Hybrid Map. Appl. Sci., 15.","DOI":"10.3390\/app15052414"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ren, L., Kang, Y., Yang, L., Jia, H., and Wang, S. (2025). Optimization Algorithm for 3D Smooth Path of Robotic Arm in Dynamic Obstacle Environments. Appl. Sci., 15.","DOI":"10.3390\/app15042116"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Ib\u00e1\u00f1ez, J.R., P\u00e9rez-del-Pulgar, C.J., and Garc\u00eda-Cerezo, A. (2021). Path Planning for Autonomous Mobile Robots: A Review. Sensors, 21.","DOI":"10.3390\/s21237898"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1002\/asjc.2778","article-title":"Potential field-based path planning for emergency collision avoidance with a clothoid curve in waypoint tracking","volume":"24","author":"Lin","year":"2022","journal-title":"Asian J. Control"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Li, H., Lu, Y., Li, Y., Zheng, S., Sun, C., Zhang, J., and Liu, L. (2024). Optimization of Model Predictive Control for Autonomous Vehicles Through Learning-Based Weight Adjustment. IEEE Trans. Intell. Transp. Syst., 1\u201312.","DOI":"10.1109\/TITS.2024.3445598"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s10846-025-02229-0","article-title":"Multi-agent Path Planning Based on Conflict-Based Search (CBS) Variations for Heterogeneous Robots","volume":"111","author":"Bai","year":"2025","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1177\/02783640122067453","article-title":"Randomized Kinodynamic Planning","volume":"20","author":"LaValle","year":"2001","journal-title":"Int. J. Robot. Res."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Chen, H., Zang, X., Zhu, Y., and Zhao, J. (2024). Hybrid Sampling-Based Path Planning for Mobile Manipulators Performing Pick and Place Tasks in Narrow Spaces. Appl. Sci., 14.","DOI":"10.3390\/app142210313"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"120254","DOI":"10.1016\/j.eswa.2023.120254","article-title":"Path planning techniques for mobile robots: Review and prospect","volume":"227","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"125206","DOI":"10.1016\/j.eswa.2024.125206","article-title":"A hybrid sampling-based RRT* path planning algorithm for autonomous mobile robot navigation","volume":"258","author":"Ganesan","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Yang, L., Li, P., Qian, S., Quan, H., Miao, J., Liu, M., Hu, Y., and Memetimin, E. (2023). Path Planning Technique for Mobile Robots: A Review. Machines, 11.","DOI":"10.3390\/machines11100980"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Qin, H., Shao, S., Wang, T., Yu, X., Jiang, Y., and Cao, Z. (2023). Review of Autonomous Path Planning Algorithms for Mobile Robots. Drones, 7.","DOI":"10.3390\/drones7030211"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/ACCESS.2014.2302442","article-title":"Sampling-Based Robot Motion Planning: A Review","volume":"2","author":"Elbanhawi","year":"2014","journal-title":"IEEE Access"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"012039","DOI":"10.1088\/1742-6596\/1211\/1\/012039","article-title":"Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for optimizing PID parameters on Autonomous Underwater Vehicle (AUV) control system","volume":"1211","author":"Herlambang","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Gul, F., Mir, I., Abualigah, L., Sumari, P., and Forestiero, A. (2021). A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions. Electronics, 10.","DOI":"10.3390\/electronics10182250"},{"key":"ref_104","first-page":"1","article-title":"Path planning and trajectory tracking control for two-wheel mobile robot","volume":"5","author":"Hassan","year":"2024","journal-title":"J. Robot. Control (JRC)"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"105535","DOI":"10.1016\/j.conengprac.2023.105535","article-title":"Robust trajectory tracking and control allocation of X-rudder AUV with actuator uncertainty","volume":"136","author":"Wang","year":"2023","journal-title":"Control Eng. Pract."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Reyhanoglu, M., and Jafari, M. (2023). A simple learning approach for robust tracking control of a class of dynamical systems. Electronics, 12.","DOI":"10.3390\/electronics12092026"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"3559","DOI":"10.1109\/TETCI.2024.3424527","article-title":"Efficient online planning and robust optimal control for nonholonomic mobile robot in unstructured environments","volume":"8","author":"Hu","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Lee, T., and Jeong, Y. (2024). A tube-based model predictive control for path tracking of autonomous articulated vehicle. Actuators, 13.","DOI":"10.3390\/act13050164"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/TIV.2023.3307737","article-title":"Hybrid model predictive control for unmanned ground vehicles","volume":"9","author":"Khan","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1109\/TITS.2022.3230680","article-title":"An improved model predictive control-based trajectory planning method for automated driving vehicles under uncertainty environments","volume":"24","author":"Qie","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"5647","DOI":"10.1109\/TII.2023.3331772","article-title":"Trajectory tracking control of autonomous underwater vehicles using improved tube-based model predictive control approach","volume":"20","author":"Hao","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Heshmati-alamdari, S., Nikou, A., and Dimarogonas, D.V. (2019, January 11\u201313). Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles. Proceedings of the 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France.","DOI":"10.1109\/CDC40024.2019.9030165"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Ou, Y., Cai, Y., Sun, Y., and Qin, T. (2024). Autonomous navigation by mobile robot with sensor fusion based on deep reinforcement learning. Sensors, 24.","DOI":"10.3390\/s24123895"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"U\u0161inskis, V., Nowicki, M., Dzedzickis, A., and Bu\u010dinskas, V. (2025). Sensor-Fusion Based Navigation for Autonomous Mobile Robot. Sensors, 25.","DOI":"10.3390\/s25041248"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"V\u00e1sconez, J.P., Calder\u00f3n-D\u00edaz, M., Brice\u00f1o, I.C., Pantoja, J.M., and Cruz, P.J. (2024). A Behavior-Based Fuzzy Control System for Mobile Robot Navigation: Design and Assessment. International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-48858-0_33"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"437","DOI":"10.18196\/jrc.v6i1.25553","article-title":"Type-2 Fuzzy Logic-Based Robot Navigation in Uncertain Environments: Simulation and Real-World Implementation","volume":"6","author":"Hachani","year":"2025","journal-title":"J. Robot. Control (JRC)"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"102254","DOI":"10.1016\/j.jksuci.2024.102254","article-title":"Deep reinforcement learning-based local path planning in dynamic environments for mobile robot","volume":"36","author":"Tao","year":"2024","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Bi, Y., and Fang, X. (2025). A Hybrid Path Planning Framework Integrating Deep Reinforcement Learning and Variable-Direction Potential Fields. Mathematics, 13.","DOI":"10.3390\/math13142312"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"67459","DOI":"10.1109\/ACCESS.2025.3557394","article-title":"A hybrid deep learning model for UAV path planning in dynamic environments","volume":"13","author":"Zhang","year":"2025","journal-title":"IEEE Access"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Zhu, W., and Chen, Z. (2025). Research on Path Planning for Mobile Charging Robots Based on Improved A* and DWA Algorithms. Electronics, 14.","DOI":"10.3390\/electronics14122318"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"32271","DOI":"10.1109\/JIOT.2025.3576911","article-title":"Path Planning for Transoceanic Underwater Glider Based on Hybrid Reinforcement Learning Algorithm","volume":"12","author":"Li","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"12643","DOI":"10.1038\/s41598-025-96614-2","article-title":"Simulation-based review of classical, heuristic, and metaheuristic path planning algorithms","volume":"15","author":"Ugwoke","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Zhang, T., Fan, J., Zhou, N., and Gao, Z. (2024). Highly Self-Adaptive Path-Planning Method for Unmanned Ground Vehicle Based on Transformer Encoder Feature Extraction and Incremental Reinforcement Learning. Machines, 12.","DOI":"10.3390\/machines12050289"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Lee, K., Im, E., and Cho, K. (2024). Mission-Conditioned Path Planning with Transformer Variational Autoencoder. Electronics, 13.","DOI":"10.3390\/electronics13132437"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"10233","DOI":"10.1109\/TII.2023.3240585","article-title":"Transformer-based imitative reinforcement learning for multirobot path planning","volume":"19","author":"Chen","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"8794","DOI":"10.1109\/LRA.2024.3450305","article-title":"Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making","volume":"9","author":"Zhuang","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"12134","DOI":"10.1109\/TASE.2025.3539340","article-title":"Safe and Interpretable Human-Like Planning With Transformer-Based Deep Inverse Reinforcement Learning for Autonomous Driving","volume":"22","author":"Nan","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.2514\/1.G008302","article-title":"Transformer-based tight constraint prediction for efficient powered descent guidance","volume":"48","author":"Briden","year":"2025","journal-title":"J. Guid. Control Dyn."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"8463","DOI":"10.1109\/TIE.2024.3525117","article-title":"Attention-based UAV trajectory optimization for wireless power transfer-assisted IoT systems","volume":"72","author":"Dong","year":"2025","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1631\/jzus.A2400317","article-title":"Technical development and future prospects of cooperative terminal guidance based on knowledge graph analysis: A review","volume":"26","author":"Liu","year":"2025","journal-title":"J. Zhejiang Univ.-Sci. A"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"104748","DOI":"10.1016\/j.robot.2024.104748","article-title":"Cooperative path planning study of distributed multi-mobile robots based on optimised ACO algorithm","volume":"179","author":"Cai","year":"2024","journal-title":"Robot. Auton. Syst."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"8470","DOI":"10.1109\/JSEN.2024.3516124","article-title":"Cooperative Path Planning of Multiple Unmanned Aerial Vehicles Using Cylinder Vector Particle Swarm Optimization With Gene Targeting","volume":"25","author":"Huang","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_133","first-page":"10","article-title":"A Comprehensive Review of Path Planning Techniques for Mobile Robot Navigation in Known and Unknown Environments","volume":"11","author":"Nasti","year":"2025","journal-title":"Int. J. Comput. Exp. Sci. Eng."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11227-025-06960-1","article-title":"Multi-robot Collaborative 3D Path Planning Based On Game Theory and Particle Swarm Optimization Hybrid Method","volume":"81","author":"Qiu","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_135","first-page":"101343","article-title":"A systematic review on recent advances in autonomous mobile robot navigation","volume":"40","author":"Loganathan","year":"2023","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"112002","DOI":"10.1088\/1361-6501\/ad66f5","article-title":"Hybrid algorithms in path planning for autonomous navigation of unmanned aerial vehicle: A comprehensive review","volume":"35","author":"Minh","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Abdulsaheb, J.A., and Kadhim, D.J. (2023). Classical and heuristic approaches for mobile robot path planning: A survey. Robotics, 12.","DOI":"10.3390\/robotics12040093"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Zhang, C., Wu, Z., Li, Z., Xu, H., Xue, Z., and Qian, R. (2024, January 6\u201310). Multi-agent Reinforcement Learning-Based UAV Swarm Confrontation: Integrating QMIX Algorithm with Artificial Potential Field Method. Proceedings of the 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kuching, Malaysia.","DOI":"10.1109\/SMC54092.2024.10832089"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Kong, G., Cai, J., Gong, J., Tian, Z., Huang, L., and Yang, Y. (2022). Cooperative following of multiple autonomous robots based on consensus estimation. Electronics, 11.","DOI":"10.3390\/electronics11203319"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1007\/s13369-019-04193-y","article-title":"Hybridization of Kidney-Inspired and sine\u2013cosine algorithm for multi-robot path planning","volume":"45","author":"Das","year":"2020","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.neucom.2016.05.057","article-title":"A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment","volume":"207","author":"Das","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s12293-015-0160-3","article-title":"A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach","volume":"7","author":"Mohanty","year":"2015","journal-title":"Memetic Comput."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"101938","DOI":"10.1016\/j.jocs.2022.101938","article-title":"An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse","volume":"67","author":"Lin","year":"2023","journal-title":"J. Comput. Sci."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"110063","DOI":"10.1016\/j.compag.2025.110063","article-title":"Efficient motion planning for chili flower pollination mechanism based on BI-RRT","volume":"232","author":"Ni","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TSSC.1968.300136","article-title":"A Formal Basis for the Heuristic Determination of Minimum Cost Paths","volume":"4","author":"Hart","year":"1968","journal-title":"IEEE Trans. Syst. Sci. Cybern."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/BF01386390","article-title":"A note on two problems in connexion with graphs","volume":"1","author":"Dijkstra","year":"1959","journal-title":"Numer. Math."},{"key":"ref_147","first-page":"85","article-title":"Optimal path forecasting of an autonomous mobile robot agent using breadth first search algorithm","volume":"14","author":"Subramanian","year":"2014","journal-title":"Int. J. Mech. Mechatron. Eng."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1145\/367766.368168","article-title":"Algorithm 97: Shortest path","volume":"5","author":"Floyd","year":"1962","journal-title":"Commun. ACM"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1137\/0201010","article-title":"Depth-First Search and Linear Graph Algorithms","volume":"1","author":"Tarjan","year":"1972","journal-title":"SIAM J. Comput."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1090\/qam\/102435","article-title":"ON A ROUTING PROBLEM","volume":"16","author":"Bellman","year":"1958","journal-title":"Q. Appl. Math."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/70.508439","article-title":"Probabilistic roadmaps for path planning in high-dimensional configuration spaces","volume":"12","author":"Kavraki","year":"1996","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Gammell, J.D., Srinivasa, S.S., and Barfoot, T.D. (2015, January 26\u201330). Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, DC, USA.","DOI":"10.1109\/ICRA.2015.7139620"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Karaman, S., Walter, M.R., Perez, A., Frazzoli, E., and Teller, S. (2011, January 9\u201313). Anytime motion planning using the RRT. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980479"}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/5\/87\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:34:48Z","timestamp":1760034888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/5\/87"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,28]]},"references-count":153,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["jsan14050087"],"URL":"https:\/\/doi.org\/10.3390\/jsan14050087","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,28]]}}}